code: def _preprocess_features(features):
      rowsum = np.array(features.sum(1))
      r_inv = np.power(rowsum, -1).flatten()
      r_inv[np.isinf(r_inv)] = 0.0
      r_mat_inv = sp.diags(r_inv)
      features = r_mat_inv.dot(features)
      return features
  
imports: import os
  import os.path
  import networkx
  import numpy
  import requests
  import scipy.sparse
  from sklearn.model_selection import train_test_split
  from spektral.data import Dataset, Graph
  from spektral.datasets.utils import DATASET_FOLDER
  from spektral.utils.io import load_binary
label_desc: Use image node_id 3 for calling a global function with example usage: _preprocess_features(features) and returns: features